Abstract
Recurrent neural networks (RNNs) offer state-of-the-art (SOTA) performance on a variety of important prediction tasks with clinical time series data. However, meaningful translation to actionable decisions requires capability to quantify confidence in the predictions, and to address the inherent ambiguities in the data and the associated modeling process. We propose a Bayesian LSTM framework using Bayes by Backprop to characterize both modelling (epistemic) and data related (aleatoric) uncertainties in prediction tasks for clinical time series data. We evaluate our approach on mortality prediction tasks with two public Intensive Care Unit (ICU) data sets, namely, the MIMIC-III and the PhysioNet 2012 collections. We demonstrate the potential for improved performance over SOTA methods, and characterize aleatoric uncertainty in the setting of noisy features. Importantly, we demonstrate how our uncertainty estimates could be used in realistic prediction scenarios to better interpret the reliability of the data and the model predictions, and improve relevance for decision support.
S. Ramasamy and P. Krishnaswamy - Equal Contribution
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Acknowledgments
Research efforts were supported by funding and infrastructure from A*STAR, Singapore (Grant Nos. SSF A1818g0044 and IAF H19/01/a0/023). The authors would also like to acknowledge inputs from Wu Jiewen and Ivan Ho Mien on the clinical datasets. We also thank Vijay Chandrasekhar for his support, and are grateful for the constructive feedback from the anonymous reviewers.
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Guo, Y., Liu, Z., Ramasamy, S., Krishnaswamy, P. (2021). Uncertainty Characterization for Predictive Analytics with Clinical Time Series Data. In: Shaban-Nejad, A., Michalowski, M., Buckeridge, D.L. (eds) Explainable AI in Healthcare and Medicine. Studies in Computational Intelligence, vol 914. Springer, Cham. https://doi.org/10.1007/978-3-030-53352-6_7
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